31-GEP Assay May Help to Extend Survival in Cutaneous Melanoma

Aaron Farberg, MD

Double Board-Certified Dermatologist and Fellowship-trained Mohs Surgeon

A 3-year follow-up study assessing the impact of the DecisionDx-Melanoma 31-gene expression profile (31-GEP) assay on survival in patients with cutaneous melanoma turned up “remarkable findings,” according to Aaron Farberg, MD.

In an interview with CancerNetwork^®, Farberg, a double board-certified dermatologist and fellowship-trained Mohs surgeon who specializes in skin cancer, inflammatory diseases, and cosmetic dermatology, spoke to the significance of the study’s findings, how the 31-GEP assay is being used in clinical practice, and the excitement of waiting for the 5-year and 10-year follow-up data.

“This is the first and largest study utilizing the Surveillance, Epidemiology, and End Results (SEER) database and gene expression profiling in cutaneous melanoma,” Farberg said. “Also, an important point about this test is that it provides objective information based on the tumor biology of our patient, rather than what can sometimes be subjective, clinical, and pathologic features. This is personalized medicine at its current best, and I look forward to the collaboration with the National Cancer Institute (NCI) to enhance this technology even further.”

Data from the study indicated that those with a 31-GEP class 1A result had a 3-year melanoma specific survival (MSS) rate of 99.7% (95% CI, 99.4%-100.0%) compared with 97.1% (95% CI, 95.7%-98.6%) in those with a class 1B/2A result and 89.6% (95% CI, 86.3%-93.1%) in those with a class 2B result. The 3-year overall survival (OS) rate in each respective group was 96.6% (95% CI, 95.7%-97.6%) vs 90.2% (95% CI, 87.7%-92.8%) vs 79.4% (95% CI, 75.4%-83.6%).

CancerNetwork^®: Could you discuss how the 31-GEP test works and what sets it apart from other assays within the melanoma space?

Farberg: The 31-GEP test evaluates the molecular makeup—the genes—of an invasive melanoma. Essentially, when patients are diagnosed with melanoma, they then become staged based on clinical and pathological factors. The purpose of staging is to group patients with similar characteristics that will also then be matched to similar outcomes. In patients who are prone or have a bad prognosis, that leads to more intense management changes. The 31-GEP test looks deeper, into the biology of a patient’s tumor, to evaluate how aggressive it is. If a patient has a lower-risk tumor based on the 31-GEP result , then maybe you don’t have to do as much to treat or manage this patient.

What the 31-GEP does is take a sample of that patient’s melanoma, evaluate 31 different genes, and look at them using a type of artificial intelligence. It is able to provide guidance and provide an output, which then helps me, the clinician, determine whether or not this patient’s specific melanoma is low risk, intermediate risk, or high risk based on the actual behavior of the individual tumor.

What efficacy has read out in support of this assay?

This is a groundbreaking, very exciting data set that was presented looking at the utilization of the 31-GEP or DecisionDx-Melanoma test. With all studies, it starts with having the best data set, the best patient population, and this study utilized the SEER database. There are lots of peer reviewed publications showing that the 31-GEP works, but [we wanted to know] if it works in this dataset that’s representative of the entire United States. That’s exactly what this study did.

It looked at patients who had invasive melanoma, and then also underwent the 31-GEP testing. [The study] asked the questions of ‘does the test work? Does the test stratify these patients? In other words, does it help me, the clinician, understand whether or not they’re low, intermediate or high risk?’ Without any doubt, in this unselected, prospectively collected, representative cohort of patients, the 31-GEP was able to stratify these patients who are diagnosed with invasive melanoma. What’s even more impactful is that they then ask the questions,‘Did [the test] have any impact on these patients to help them live longer? Did it work? Did it really impact the overall outcome?’

The issue was that over a decade ago, we were limited in the number of treatments that we had for melanoma. Since then, thanks to progress within research and the field of medicine, we now have various imaging techniques and various treatment modalities that can actually have an impact on this disease. [This begs the] question: would a better understanding of these patients’ risk profile really lead to better outcomes? What [investigators] did is they took a group of patients who had the 31-GEP test ordered, and compared it with another group that didn’t have the 31-GEP test. They matched these groups by every covariant that exists in the SEER database. For the most part, these were equivalent groups; they matched 3 patients who didn’t get the test for every 1 patient who did receive the 31-GEP test. There was an overall improvement in MSS and then OS [in the 31-GEP test cohort].

Are there any plans for further research on 31-GEP?

The data set that was published showed these remarkable findings at just 3 years of data analysis. We’re all going to be looking forward to the 5-year data. But again, it’s incredibly impactful that we’re able to see these findings at only 3 years. But the question will be, ‘How does this stand up after 5 years, and after 10 years?’ It just takes time to accumulate these data sets. Right now, there are over 4500 patients who were evaluated in this study. As time goes on, those numbers will only increase. It really provides me, the clinician, the confidence to utilize this type of testing to improve the outcomes of my patients.

What do you hope that your colleagues within the oncology space take away from this conversation?

If you use the DecisionDx-Melanoma Test, you’ll be helping our patients with invasive melanoma live longer. This task provides enhanced stratification and improved understanding of each of your patients’ individual and personalized risk, [which can help] you make a collaborative decision. It can inform collaborative decision-making between you and your patient.

Reference

Bailey CN, Martin BJ, Petkov VI, et al. 31-gene expression profile testing in cutaneous melanoma and survival outcomes in a population-based analysis: a SEER collaboration. JCO Prec. 2023;7:e2300044. doi:10.1200/PO.23.00044